A fuzzy ontology: based framework for reasoning in visual video content analysis and indexing

ABSTRACTMultimedia indexing systems based on semantic concept detectors are incomplete in the semantic sense. We can improve the effectiveness of these systems by using knowledge-based approaches which utilize semantic knowledge. In this paper, we propose a novel and efficient approach to enhance semantic concept detection in multimedia content, by exploiting contextual information about concepts from visual modality. First, a semantic knowledge is extracted via a contextual annotation framework. Second, a Fuzzy ontology is proposed to represent the fuzzy relationships (roles and rules) among every context and its semantic concepts. We use an abduction engine based on βeta function as a membership function for fuzzy rules. Third, a deduction engine is used to handle richer results in our video indexing system by running the proposed fuzzy ontology. Experiments on TRECVID 2010 benchmark have been performed to evaluate the performance of this approach. The obtained results show consistent improvement in semantic concepts detection, when a context space is used, and a good degree of indexing effectiveness as compared to existing approaches.